{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.2 循环神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4.1\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 5.2633, -3.2288,  0.6037, -1.3321],\n",
       "        [ 9.4012, -6.7830,  1.0630, -0.1809],\n",
       "        [ 7.0355, -2.2361,  0.7469, -3.4667]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, W_xh = torch.randn(3, 1), torch.randn(1, 4)\n",
    "H, W_hh = torch.randn(3, 4), torch.randn(4, 4)\n",
    "torch.matmul(X, W_xh) + torch.matmul(H, W_hh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 5.2633, -3.2288,  0.6037, -1.3321],\n",
       "        [ 9.4012, -6.7830,  1.0630, -0.1809],\n",
       "        [ 7.0355, -2.2361,  0.7469, -3.4667]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.matmul(torch.cat((X, H), dim=1), torch.cat((W_xh, W_hh), dim=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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